The long-term objective is to enable rapid, sensitive, and low-cost detection, and reliable quantification of structural changes in tissue caused by agents such as drugs, toxins, hormones, diseases, and physical/chemical stimuli. Such studies form the core of pharmaceutical research, toxicology, human- and animal-used product testing for safety and effectiveness, as well as a large body of basic research in biology and medicine. In these studies, it is necessary to perform comparative cytometry on two batches of tissue - one of which is normal, and the other is transformed by the agent of interest. When the changes are subtle (e.g., cancer), it is important to process large amounts of tissue. Currently, all this is done manually, so the methods are slow, tedious, expensive, and lacking in quantitative accuracy and sensitivity. Often, important studies are abandoned or not attempted for these reasons. The proposed research will result in a method for large-scale, fully automated tissue-level change detection and quantification. It is based on 3-D microscopy of thick (20-500 microm), wide (1-10mm) tissue samples in a series of overlapping windows, followed by automated 3-D image analysis. This produces a database of 3-D cytometric measurements that are analyzed by statistical methods to detect and quantify changes. PROPOSED COMMERCIAL APPLICATION: The resulting software product is expected to be of immediate and long- term interest to pharmaceutical companies, companies that must evaluate common everyday products (e.g., soaps, shampoos, cosmetics, laxatives, ointments) for health risks and effectiveness, state health departments (esp. toxicology), most major research hospitals, (esp. departments of pathology), and biology departments of major universities worldwide.